n unsupervised learning, which of the following methods can be used for dimensionality reduction?a)Principal Component Analysis (PCA)b)Random Forestc)AdaBoostd)Gradient Boosting
Question
n unsupervised learning, which of the following methods can be used for dimensionality reduction?a)Principal Component Analysis (PCA)b)Random Forestc)AdaBoostd)Gradient Boosting
Solution
In unsupervised learning, Principal Component Analysis (PCA) can be used for dimensionality reduction. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. This technique is widely used in machine learning to ease the computational burden and eliminate noise.
Random Forest, AdaBoost, and Gradient Boosting are machine learning algorithms used for classification and regression problems, not for dimensionality reduction. These methods create a strong predictive model by combining the predictions of multiple weak models. They do not aim to reduce the dimensionality of the data.
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